From Rules to Transformers: A Deep Learning Approach for Arabic Natural Language Interfaces to Databases

Natural language interfaces to databases (NLIDBs) enable users to communicate with databases using natural everyday language rather than difficult query languages. This study presents a new approach using deep learning techniques to improve the robustness and accessibility of Arabic NLIDB systems through a new end-to-end framework. A Transformer-based architecture is proposed, in which AraT5 is utilized to translate Arabic Natural Language Queries (ANLQs) into structured JSON Logical Query (JLQ) representations, subsequently converting these into executable SQL statements. Traditional rule-based systems are surpassed by this approach, as semantic understanding is leveraged instead of grammatical pattern matching. Consequently, the morphological complexity and dialectical variations of Arabic are more effectively handled. This neural semantic parsing approach demonstrates a deep understanding of query intent, moving beyond surface-level pattern matching. Experimental evaluation on a large-scale, multi-domain curated dataset of 50,000 query pairs demonstrates superior performance, with 85.2% exact match accuracy for JLQ generation and 89.8% SQL execution accuracy. The findings indicate that Transformer-based approaches offer substantial improvements in translation accuracy compared to conventional rule-induction methods.

@article{Laila2026,
  title     = {From Rules to Transformers: A Deep Learning Approach for Arabic Natural Language Interfaces to Databases},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {17},
  number    = {3},
  year      = {2026},
  publisher = {The Science and Information Organization},
  author    = {Dahr Laila and Sahib Mohamed Rida and Er-Raha Brahim},
  doi       = {10.14569/IJACSA.2026.0170313},
  url       = {https://doi.org/10.14569/IJACSA.2026.0170313}
}